A multiple-input multiple-output (MIMO) system uses multiple transmit and receive antennas. The MIMO system can increase channel transmission capacity in proportion to the number of antennas without allocating additional frequency or transmission power, as compared to a system using a single antenna. The channel capacity of the MIMO system mainly depends on a signal detecting method used in a receiver in order to recover blocks of transmitted symbols. It is important to design the signal detecting method of the MIMO system so that high performance, low complexity and detection delay are achieved. This is a particular problem for doubly-selective channels that are subject to time-varying fading and multi-path delays, where both inter-carrier interference (ICI) and inter-symbol interference (ISI) occur.
An example of the signal detecting method of the MIMO system may include a maximum likelihood (ML) detecting method, a sphere decoding algorithm, a QR-decomposition with M-algorithm (QRD-M) algorithm, and so on.
Although the ML detecting method provides optimal performance in a MIMO system, the operational complexity exponentially increases when the number of transmitting antennas increases and a higher order modulation method is used. Therefore, there is a disadvantage in that the ML detecting method is not practically used.
The sphere decoding algorithm provides performance similar to that of the ML method and the significantly reduced average operation complexity as compared to the ML detecting method. However, the sphere decoding algorithm instantaneously changes complexity due to the condition number of a channel matrix and the noise dispersion. As a result, the sphere decoding algorithm represents an operational complexity similar to the ML method in a worst case scenario. In other words, the operational complexity of the sphere decoding algorithm has a large standard deviation and randomness. Therefore, it is difficult to apply the sphere decoding algorithm to applications where a mobile base station has limited power and low detection latency tolerance.
The QRD-M method is provided as a compromise between performance and complexity. In the QRD-M method, the amount of computation required to detect signals is fixed regardless of channel conditions or noise power. Therefore, the QRD-M method detecting the signals considers more information at each process, thereby making it possible to further reduce the operational complexity. In other words, when there is well-conditioned channel environment or low noise power, the QRD-M method reduces the number of remaining candidate symbols, thereby reducing operations relating to accumulated distances calculated at each branch. However, the detection performance of the QRD-M method depends on the number of selected candidates and the more the number of candidates, the larger the operational complexity becomes.